




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
	from ...onnx.config import PatchingSpec
	from ...tokenization_utils_base import PreTrainedTokenizerBase


snake_case_  :      Optional[Any]		=		logging.get_logger(__name__)

snake_case_  :      List[Any]		=		{
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}





class 					snake_case_   (     _lowerCAmelCase	):



				'''simple docstring'''
				lowerCamelCase       =  '''longformer'''





				def __init__(      self							:   Tuple      ,							__magic_name__							:   Union[List[int], int] = 512      ,							__magic_name__							:   int = 2      ,							__magic_name__							:   int = 1      ,							__magic_name__							:   int = 0      ,							__magic_name__							:   int = 2      ,							__magic_name__							:   int = 3_0522      ,							__magic_name__							:   int = 768      ,							__magic_name__							:   int = 12      ,							__magic_name__							:   int = 12      ,							__magic_name__							:   int = 3072      ,							__magic_name__							:   str = "gelu"      ,							__magic_name__							:   float = 0.1      ,							__magic_name__							:   float = 0.1      ,							__magic_name__							:   int = 512      ,							__magic_name__							:   int = 2      ,							__magic_name__							:   float = 0.02      ,							__magic_name__							:   float = 1e-12      ,							__magic_name__							:   bool = False      ,							**__magic_name__							:   str      ,							)							->			List[Any]:
											super().__init__(pad_token_id=UpperCAmelCase_      ,							**UpperCAmelCase_							)

											lowerCamelCase_			:    Optional[int]   						=      attention_window
											lowerCamelCase_			:    str   						=      sep_token_id
											lowerCamelCase_			:    List[str]   						=      bos_token_id
											lowerCamelCase_			:    Tuple   						=      eos_token_id
											lowerCamelCase_			:    Optional[int]   						=      vocab_size
											lowerCamelCase_			:    str   						=      hidden_size
											lowerCamelCase_			:    Dict   						=      num_hidden_layers
											lowerCamelCase_			:    Optional[int]   						=      num_attention_heads
											lowerCamelCase_			:    List[Any]   						=      hidden_act
											lowerCamelCase_			:    List[Any]   						=      intermediate_size
											lowerCamelCase_			:    List[str]   						=      hidden_dropout_prob
											lowerCamelCase_			:    str   						=      attention_probs_dropout_prob
											lowerCamelCase_			:    Any   						=      max_position_embeddings
											lowerCamelCase_			:    List[Any]   						=      type_vocab_size
											lowerCamelCase_			:    Union[str, Any]   						=      initializer_range
											lowerCamelCase_			:    Optional[int]   						=      layer_norm_eps
											lowerCamelCase_			:    int   						=      onnx_export





class 					snake_case_   (     _lowerCAmelCase	):



				'''simple docstring'''





				def __init__(      self							:   int      ,							__magic_name__							:   "PretrainedConfig"      ,							__magic_name__							:   str = "default"      ,							__magic_name__							:   "List[PatchingSpec]" = None							)							->			Union[str, Any]:
											super().__init__(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_							)
											lowerCamelCase_			:    Optional[int]   						=      True





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		lowerCamelCase_			:    str   						=      {0: "batch", 1: "choice", 2: "sequence"}
											else:
																		lowerCamelCase_			:    str   						=      {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											        ("global_attention_mask", dynamic_axis),
											    ]							)





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			Mapping[str, Mapping[int, str]]:
											lowerCamelCase_			:    Union[str, Any]   						=      super().outputs
											if self.task == "default":
																		lowerCamelCase_			:    Optional[Any]   						=      {0: "batch"}
											return outputs





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]							)							->			float:
											return 1e-4





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			int:
											return max(super().default_onnx_opset      ,							14							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]      ,							__magic_name__							:   "PreTrainedTokenizerBase"      ,							__magic_name__							:   int = -1      ,							__magic_name__							:   int = -1      ,							__magic_name__							:   bool = False      ,							__magic_name__							:   Optional[TensorType] = None      ,							)							->			Mapping[str, Any]:
											lowerCamelCase_			:    Any   						=      super().generate_dummy_inputs(
											    preprocessor=UpperCAmelCase_      ,							batch_size=UpperCAmelCase_      ,							seq_length=UpperCAmelCase_      ,							is_pair=UpperCAmelCase_      ,							framework=UpperCAmelCase_							)
											import torch

											# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
											# makes the export fail randomly
											lowerCamelCase_			:    List[Any]   						=      torch.zeros_like(inputs["input_ids"]							)
											# make every second token global
											lowerCamelCase_			:    Optional[int]   						=      1

											return inputs



"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				float							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				bool = False							,   )->	str:







              """simple docstring"""
              super().__init__()

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		False

              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )

              UpperCamelCase       			=		TaConfig(
                  vocab_size=UpperCAmelCase_							,   d_model=UpperCAmelCase_							,   num_heads=UpperCAmelCase_							,   d_kv=UpperCAmelCase_							,   d_ff=UpperCAmelCase_							,   dropout_rate=UpperCAmelCase_							,   feed_forward_proj=UpperCAmelCase_							,   is_decoder=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   )

              UpperCamelCase       			=		nn.ModuleList()
              for lyr_num in range(UpperCAmelCase_      ):
                     UpperCamelCase       			=		TaBlock(UpperCAmelCase_      )
                     self.encoders.append(UpperCAmelCase_      )

              UpperCamelCase       			=		TaLayerNorm(UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.token_embedder(UpperCAmelCase_      )

              UpperCamelCase       			=		encoder_input_tokens.shape[1]
              UpperCamelCase       			=		torch.arange(UpperCAmelCase_							,   device=encoder_input_tokens.device      )
              x += self.position_encoding(UpperCAmelCase_      )

              UpperCamelCase       			=		self.dropout_pre(UpperCAmelCase_      )

              # inverted the attention mask
              UpperCamelCase       			=		encoder_input_tokens.size()
              UpperCamelCase       			=		self.get_extended_attention_mask(UpperCAmelCase_							,   UpperCAmelCase_      )

              for lyr in self.encoders:
                     UpperCamelCase       			=		lyr(UpperCAmelCase_							,   UpperCAmelCase_      )[0]
              UpperCamelCase       			=		self.layer_norm(UpperCAmelCase_      )

              return self.dropout_post(UpperCAmelCase_      ), encoder_inputs_mask





